{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/drive/1tmjp68IcCnLYASeW8Hg5pHyMdeQCIwP-?usp=sharing\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "ltgr0WOdmMRF"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqMsvBANlvDv"
      },
      "source": [
        "## Prerequisites\n",
        "Before we start, make sure you have the following:\n",
        "\n",
        "- Access to a GPU (preferably A100 since videos require high sequence lengths).\n",
        "- Familiarity with Hugging Face’s Transformers library.\n",
        "- Pre-install necessary packages by running the below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-T8dupbwlvDx"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q transformers accelerate bitsandbytes peft dataset\n",
        "!pip install -q av\n",
        "!pip install -q lightning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTG2AJZT89qk"
      },
      "source": [
        "## Fine-tune VIdeo-LLaVa on MMBench dataset\n",
        "\n",
        "In this notebook, we are going to fine-tune the [Video-LLaVa](https://huggingface.co/docs/transformers/main/en/model_doc/video_llava) model on [MMBench](https://huggingface.co/datasets/OpenGVLab/MVBench) dataset which is comprised of various video-related tasks. Note that MMBench is quite small and is not made for tuning. Make sure to choose a bigger dataset for your own use-case.\n",
        "\n",
        "Video-LLaVa is an open-source multimodal model that can accept both, images and videos as input in an interleaved manner. The model architecture is pretty much similar to [LLaVa](https://huggingface.co/docs/transformers/main/en/model_doc/llava). However Video-LLaVa leverages a new universal visial encoder to seemlessly handle both visual modes. As we'll see, fine-tuning these various models is pretty similar as their API is mostly the same.\n",
        "\n",
        "The goal for the model in this notebook is to answer given multiple choice questions basedd on the video. The questions can be realetd to temporal aspects of the video, pose prediction and so on.\n",
        "Sources:\n",
        "\n",
        "* Video-LLaVa [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/video_llava)\n",
        "* Video-LLaVa [checkpoint on the hub](https://huggingface.co/LanguageBind/Video-LLaVA-7B-hf)\n",
        "\n",
        "**Note: this notebook is a direct adaptation of Niels' [LLaVa notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LLaVa/Fine_tune_LLaVa_on_a_custom_dataset_(with_PyTorch_Lightning).ipynb).**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Szf17AKL89qm"
      },
      "source": [
        "## Define variables\n",
        "\n",
        "We'll first set some variables useful througout this notebook and doo all the necessary imports."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LJtnWc3b89qn",
        "outputId": "8306b1f9-be6b-4083-f1c7-429a21984f96"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-05-22 18:06:06.577404: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-05-22 18:06:07.308550: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import av\n",
        "import re\n",
        "import bisect\n",
        "import shutil\n",
        "import numpy as np\n",
        "from nltk import edit_distance\n",
        "\n",
        "from transformers import AutoProcessor\n",
        "from transformers import BitsAndBytesConfig, VideoLlavaForConditionalGeneration\n",
        "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from huggingface_hub import snapshot_download\n",
        "from datasets import load_dataset, concatenate_datasets\n",
        "\n",
        "import lightning as L\n",
        "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
        "\n",
        "\n",
        "MAX_LENGTH = 256\n",
        "MODEL_ID = \"LanguageBind/Video-LLaVA-7B-hf\"\n",
        "REPO_ID = \"RaushanTurganbay/VideoLLava-demo\" # Change to your hf-hub repo\n",
        "\n",
        "USE_LORA = False\n",
        "USE_QLORA = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WNZQ3imilvDz"
      },
      "source": [
        "We will start vy downloading and processing the dataset. Even though MMBench is a small dataset, it still requires around 100GB to store the videos, so make sure you have enough free space.\n",
        "\n",
        "First, we will use this mapping to get the datasets because each one is a separate subset in its own folder. Then we need a few helper functions to download videos and process them to fit the model's format (8 frames each video) ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PZGzYh7KlvDz"
      },
      "outputs": [],
      "source": [
        "config2path = {\n",
        "    \"object_interaction\": \"star/Charades_v1_480\",\n",
        "    \"action_sequence\": \"star/Charades_v1_480\",\n",
        "    \"action_prediction\": \"star/Charades_v1_480\",\n",
        "    \"moving_count\": \"clevrer/video_validation\",\n",
        "    \"moving_attribute\": \"clevrer/video_validation\",\n",
        "    \"object_existence\": \"clevrer/video_validation\",\n",
        "    \"moving_direction\": \"clevrer/video_validation\",\n",
        "    \"counterfactual_inference\": \"clevrer/video_validation\",\n",
        "    \"unexpected_action\": \"FunQA_test/test\",\n",
        "    \"episodic_reasoning\": \"tvqa/frames_fps3_hq\",\n",
        "    \"action_antonym\": \"ssv2_video\",\n",
        "    \"scene_transition\": \"scene_qa/video\",\n",
        "    \"fine_grained_pose\": \"nturgbd\",\n",
        "    \"object_shuffle\": \"perception/videos\",\n",
        "    \"state_change\": \"perception/videos\",\n",
        "    \"character_order\": \"perception/videos\",\n",
        "    \"action_localization\": \"sta/sta_video\",\n",
        "    \"fine_grained_action\": \"Moments_in_Time_Raw/vi\",\n",
        "    \"egocentric_navigation\": \"vlnqa\",\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yVdfSXMGlvDz"
      },
      "outputs": [],
      "source": [
        "def read_video_pyav(video_path, start, end):\n",
        "    \"\"\"Reads a video for given start-end timestamps interval and uniformly samples 8 frames of it\"\"\"\n",
        "    container = av.open(video_path)\n",
        "    video = container.streams.get(0)[0]\n",
        "\n",
        "    av_timestamps = [\n",
        "        int(packet.pts * video.time_base) for packet in container.demux(video) if packet.pts is not None\n",
        "    ]\n",
        "\n",
        "    av_timestamps.sort()\n",
        "    start_id = bisect.bisect_left(av_timestamps, start)\n",
        "    end_id = bisect.bisect_left(av_timestamps, end)\n",
        "\n",
        "    # in case it is a very short video, lets take a longer duration and sample\n",
        "    if end_id  - start_id < 10:\n",
        "        end_id += 10\n",
        "        start_id -= 10\n",
        "\n",
        "    end_id = min(len(av_timestamps) - 1, end_id)\n",
        "    start_id = max(1, start_id)\n",
        "    indices = np.linspace(start_id, end_id, 8).astype(int)\n",
        "\n",
        "    frames = []\n",
        "    container.seek(0)\n",
        "    for i, frame in enumerate(container.decode(video=0)):\n",
        "        if i > end_id:\n",
        "            break\n",
        "        if i >= start_id and i in indices:\n",
        "            frames.append(frame)\n",
        "    assert len(frames) == 8, f\"Got {len(frames)} frames but should be 8. Check the indices: {indices};, start_id: {start_id}, end_id: {end_id}. Len of video is {len(av_timestamps)} frames.\"\n",
        "    return np.stack([x.to_ndarray(format=\"rgb24\") for x in frames])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X9-8O207lvD0"
      },
      "outputs": [],
      "source": [
        "def collate_read_video(example, path):\n",
        "    # Some datasets have a start-end interval, so we try to get it if exists. Otherwise just set a very large end timestamp\n",
        "    clip = read_video_pyav(f'{path}/{example[\"video\"]}', example.get(\"start\", 1), example.get(\"end\", 1e+10))\n",
        "    example[\"clip\"] = clip\n",
        "    return example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "321b49438e534f3a881e10f7de9528bb"
          ]
        },
        "id": "BMNQND-ilvD0",
        "outputId": "b6ebfc90-1a0e-4a4b-c494-2f9aa0a56a2d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "321b49438e534f3a881e10f7de9528bb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Fetching 40 files:   0%|          | 0/40 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Download the videos from datasets repo and unzip. Make sure you have enough free space before downloading and unzipping\n",
        "videos = snapshot_download(repo_id=\"OpenGVLab/MVBench\", allow_patterns=\"*\", repo_type=\"dataset\")\n",
        "for zip_file in os.listdir(f\"{videos}/video\"):\n",
        "    if zip_file.endswith(\".zip\"):\n",
        "        shutil.unpack_archive(f\"{videos}/video/{zip_file}\", f\"{videos}/videos_unzipped/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "yD9StuiIlvD0"
      },
      "outputs": [],
      "source": [
        "# Load each config and save in a mapping\n",
        "config2ds = {}\n",
        "for config, path in config2path.items():\n",
        "    ds = load_dataset(\"OpenGVLab/MVBench\", config, split=\"train\")\n",
        "    ds = ds.map(collate_read_video, batched=False, fn_kwargs={\"path\": f\"{videos}/videos_unzipped/{path}\"})\n",
        "    config2ds[config] = ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GRTrOYp6lvD0",
        "outputId": "7c90aeec-491a-4f00-de39-43aa0d8d2546"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\n",
            "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "processor = AutoProcessor.from_pretrained(MODEL_ID)\n",
        "processor.tokenizer.padding_side = \"right\" # during training, one always uses padding on the right"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtxYp7h2lvD0"
      },
      "source": [
        "## Custom Dataset Class\n",
        "\n",
        "In the next step, we'll define a custom dataset class and the necessary functions to prepare our data for fine-tuning the Video-LLaVA model. The VideoLlavaDataset class extends the [PyTorch Dataset](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) class to facilitate loading and processing \"MMBench\". This class will handle the conversion of dataset samples into the format required for training and evaluation by preparing a prompt and making array from videos.\n",
        "\n",
        "NOTE: Video-LLaVa accepts videos in one of the following formats:\n",
        "- an array or tensor of shape: (batch-size, frames, channel, height, width) where batch-size is an optional dimension\n",
        "- a list of arrays of shape: (frames, channel, height, width)\n",
        "- a nested list of video frames, where each frame is an image\n",
        "\n",
        "\n",
        "Next, we define collate functions to handle the batching of data during training and evaluation. These functions ensure that the input data is properly formatted and padded.\n",
        "\n",
        "It's only here that we're going to use the processor to turn the (video, target token sequence) into the format that the model expects (which is pixel_values, input_ids etc.). The reason we do that here is because it allows for dynamic padding of the batches: each batch contains ground truth sequences of varying lengths. By only using the processor here, we will pad the input_ids up to the largest sequence in the batch.\n",
        "\n",
        "We also decide to limit the length of the text tokens (input_ids) to a max length due to memory constraints, feel free to expand if your target token sequences are longer (I'd recommend plotting the average token length of your dataset to determine the optimal value).\n",
        "\n",
        "The formatting of the input_ids is super important: we need to respect a so-called [chat template](https://huggingface.co/docs/transformers/main/en/chat_templating). As of now, Video-LLaVa does not yet support chat templates, so we manually write down the prompt in the correct format (which starts with USER and ends with ASSISTANT).You could also omit this and just train the model on (video, instruction) pairs without text prompt.\n",
        "\n",
        "Labels are created for the model by simply copying the inputs to the LLM (input_ids), but with padding tokens replaced by the ignore index of the loss function. This ensures that the model doesn't need to learn to predict padding tokens (used to batch examples together).\n",
        "\n",
        "Why are the labels a copy of the model inputs, you may ask? The model will internally shift the labels one position to the right so that the model will learn to predict the next token. This can be seen here.\n",
        "\n",
        "The collate function for evaluation is different, since there we only need to feed the prompt to the model, as we'll use the `generate()` method to autoregressively generate a completion."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "un_H3-pPlvD0"
      },
      "outputs": [],
      "source": [
        "class VideoLlavaDataset(Dataset):\n",
        "    \"\"\"\n",
        "    PyTorch Dataset for VideoLlavaDataset. This class takes a HuggingFace Dataset as input.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataset: str,\n",
        "    ):\n",
        "        super().__init__()\n",
        "        self.dataset = dataset\n",
        "        self.id2choice = {0: \"A\", 1: \"B\", 2: \"C\", 3: \"D\"}\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return len(self.dataset)\n",
        "\n",
        "    def __getitem__(self, idx: int):\n",
        "        sample = self.dataset[idx]\n",
        "        # cast to np because ds.map() casted everything to list, and the processor does not work list format\n",
        "        clip = np.array(sample[\"clip\"])\n",
        "\n",
        "        question, candidates = sample[\"question\"], sample[\"candidates\"]\n",
        "        answer = candidates.index(sample[\"answer\"])\n",
        "        answer = self.id2choice[answer]\n",
        "\n",
        "        mult_choice = \"\"\n",
        "        for i, choice in enumerate(candidates):\n",
        "            mult_choice += f\"{self.id2choice[i]}. {choice}; \"\n",
        "\n",
        "        # Prepare a prompt template, can be changed depeding on the dataset and use-cases\n",
        "        prompt = f\"USER: <video>\\nAnswer the following multiple choice question based on the video. \" \\\n",
        "                f\"Question: {question}\\n {mult_choice}\\n ASSISTANT: Answer: {answer}\"\n",
        "\n",
        "        return prompt, clip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQ6rlxudlvD1"
      },
      "outputs": [],
      "source": [
        "def train_collate_fn(examples):\n",
        "    videos = []\n",
        "    texts = []\n",
        "    texts, videos = list(zip(*examples))\n",
        "\n",
        "    batch = processor(text=texts, videos=videos, padding=True, truncation=True, max_length=MAX_LENGTH, return_tensors=\"pt\")\n",
        "\n",
        "    labels = batch[\"input_ids\"].clone()\n",
        "    labels[labels == processor.tokenizer.pad_token_id] = -100\n",
        "    batch[\"labels\"] = labels\n",
        "\n",
        "    input_ids = batch[\"input_ids\"]\n",
        "    attention_mask = batch[\"attention_mask\"]\n",
        "    pixel_values_videos = batch[\"pixel_values_videos\"]\n",
        "    labels = batch[\"labels\"]\n",
        "\n",
        "    return input_ids, attention_mask, pixel_values_videos, labels\n",
        "\n",
        "\n",
        "def eval_collate_fn(examples):\n",
        "    # We only feed the prompt to the model\n",
        "    videos = []\n",
        "    texts = []\n",
        "    texts, videos = list(zip(*examples))\n",
        "    texts = [text[:-2] for text in texts]\n",
        "\n",
        "    batch = processor(text=texts, videos=videos, padding=True, truncation=True, max_length=MAX_LENGTH, return_tensors=\"pt\")\n",
        "\n",
        "    input_ids = batch[\"input_ids\"]\n",
        "    attention_mask = batch[\"attention_mask\"]\n",
        "    pixel_values_videos = batch[\"pixel_values_videos\"]\n",
        "    answer_choice = [texts[-1] for text in texts]\n",
        "\n",
        "    return input_ids, attention_mask, pixel_values_videos, answer_choice"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sH2oWArlvD1"
      },
      "source": [
        "## Combining and Splitting the Dataset\n",
        "We'll combine multiple datasets, shuffle them, and then split them into training and test sets. This ensures that our model is trained on a diverse and representative sample of the data.\n",
        "\n",
        "As oftentimes, we get a DatasetDict which is a dictionary containing 3 splits, one for training, validation and testing. Each split has 2 features, an image and a corresponding ground truth.\n",
        "\n",
        "Let's check the some training example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DenEK2IqlvD1"
      },
      "outputs": [],
      "source": [
        "datasets_combined = concatenate_datasets(list(config2ds.values()))\n",
        "datasets_combined = datasets_combined.shuffle(seed=42)\n",
        "dataset = datasets_combined.train_test_split(test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qZb9QggAlvD1",
        "outputId": "7db0044a-aa9e-4d42-f3ff-10e995131831"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['video', 'question', 'end', 'candidates', 'accurate_end', 'accurate_start', 'start', 'answer', 'clip'],\n",
              "        num_rows: 1280\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['video', 'question', 'end', 'candidates', 'accurate_end', 'accurate_start', 'start', 'answer', 'clip'],\n",
              "        num_rows: 320\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X_DDgpOulvD1"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import animation\n",
        "from IPython.display import HTML\n",
        "\n",
        "\n",
        "example = dataset['train'][0]\n",
        "clip = example[\"clip\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aLtCWizwlvD1",
        "outputId": "cefb5908-d399-4d7a-de9b-6d323f4a847a"
      },
      "outputs": [
        {
          "data": {
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              "dmhkAAAAAAAAAAAAAAAAAAAD6AAAAyAAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAB\n",
              "AAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAAArh0cmFrAAAA\n",
              "XHRraGQAAAADAAAAAAAAAAAAAAABAAAAAAAAAyAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAA\n",
              "AAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAoAAAAHgAAAAAAAkZWR0cwAAABxlbHN0AAAAAAAAAAEA\n",
              "AAMgAAAIAAABAAAAAAIwbWRpYQAAACBtZGhkAAAAAAAAAAAAAAAAAAAoAAAAIABVxAAAAAAALWhk\n",
              "bHIAAAAAAAAAAHZpZGUAAAAAAAAAAAAAAABWaWRlb0hhbmRsZXIAAAAB221pbmYAAAAUdm1oZAAA\n",
              "AAEAAAAAAAAAAAAAACRkaW5mAAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAAAZtzdGJsAAAA\n",
              "s3N0c2QAAAAAAAAAAQAAAKNhdmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAoAB4ABIAAAASAAA\n",
              "AAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMWF2Y0MBZAAW/+EA\n",
              "GGdkABas2UCgPaEAAAMAAQAAAwAUDxYtlgEABmjr48siwAAAABx1dWlka2hA8l8kT8W6OaUbzwMj\n",
              "8wAAAAAAAAAYc3R0cwAAAAAAAAABAAAACAAABAAAAAAUc3RzcwAAAAAAAAABAAAAAQAAAFBjdHRz\n",
              "AAAAAAAAAAgAAAABAAAIAAAAAAEAABQAAAAAAQAACAAAAAABAAAAAAAAAAEAAAQAAAAAAQAACAAA\n",
              "AAABAAAMAAAAAAEAAAQAAAAAHHN0c2MAAAAAAAAAAQAAAAEAAAAIAAAAAQAAADRzdHN6AAAAAAAA\n",
              "AAAAAAAIAAAqnAAAE40AAA3sAAAK4wAACmAAAA+7AAAPDgAAC7MAAAAUc3RjbwAAAAAAAAABAAAA\n",
              "LAAAAGJ1ZHRhAAAAWm1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAA\n",
              "LWlsc3QAAAAlqXRvbwAAAB1kYXRhAAAAAQAAAABMYXZmNTguMjkuMTAw\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# np array with shape (frames, height, width, channels)\n",
        "video = np.array(clip)\n",
        "\n",
        "fig = plt.figure()\n",
        "im = plt.imshow(video[0,:,:,:])\n",
        "\n",
        "plt.close() # this is required to not display the generated image\n",
        "\n",
        "def init():\n",
        "    im.set_data(video[0,:,:,:])\n",
        "\n",
        "def animate(i):\n",
        "    im.set_data(video[i,:,:,:])\n",
        "    return im\n",
        "\n",
        "anim = animation.FuncAnimation(fig, animate, init_func=init, frames=video.shape[0],\n",
        "                               interval=100)\n",
        "HTML(anim.to_html5_video())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2rdNZDG5lvD1",
        "outputId": "38039271-d891-432e-d221-5a36996e6df7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('Which object was eaten by the person?',\n",
              " ['The refrigerator.', 'The medicine.', 'The picture.', 'The sandwich.'],\n",
              " 'The medicine.')"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example[\"question\"], example[\"candidates\"], example[\"answer\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsc4WadilvD1"
      },
      "source": [
        "And now we wrap it in the Pytorch Datasets class and print one example as sanity check."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZXPeOA34lvD1"
      },
      "outputs": [],
      "source": [
        "train_dataset = VideoLlavaDataset(dataset[\"train\"])\n",
        "eval_dataset = VideoLlavaDataset(dataset[\"test\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AeI7LaF4lvD1"
      },
      "outputs": [],
      "source": [
        "prompt, clip = train_dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fzcocRe1lvD1",
        "outputId": "b3948347-d146-432e-c8da-7a3ca7e83398"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'USER: <video>\\nAnswer the following multiple choice question based on the video. Question: Which object was eaten by the person?\\n A. The refrigerator.; B. The medicine.; C. The picture.; D. The sandwich.; \\n ASSISTANT: Answer: B'"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prompt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pd4mrynzlvD2"
      },
      "source": [
        "## Load model\n",
        "Next, we're going to load the Video-LLaVa model from the hub. This is a model with about 7 billion trainable parameters (as it combines a LLaMa-7B language model with a relatively low-parameter vision encoder). Do note that we load a model here which already has undergone supervised fine-tuning (SFT) on VideoChat instruction dataset. We can benefit from the fine-tuning that the model already has undergone.\n",
        "\n",
        "## Full fine-tuning, LoRa and Q-LoRa\n",
        "As this model has 7 billion trainable parameters, that's going to have quite an impact on the amount of memory used. For reference, fine-tuning a model using the AdamW optimizer (which is often used to optimize neural networks) with mixed precision, you need about 18 times the amount of parameters in GB of GPU RAM. So in this case, we would need 18x7 billion bytes = 126 GB of GPU RAM if we want to update all the parameters of the model!! That's huge right? And for most people infeasible.\n",
        "\n",
        "Luckily, some clever people came up with the LoRa method (LoRa is short for low-rank adapation). It allows to just freeze the existing weights and only train a couple of adapter layers on top of the base model. Hugging Face offers the separate [PEFT library](https://huggingface.co/docs/peft/main/en/index) for easy use of LoRa, along with other Parameter-Efficient Fine-Tuning methods (that's where the name PEFT comes from).\n",
        "\n",
        "Moreover, one can not only freeze the existing base model but also quantize it (which means, shrinking down its size). A neural network's parameters are typically saved in either float32 (which means, 32 bits or 4 bytes are used to store each parameter value) or float16 (which means, 16 bits or half a byte - also called half precision). However, with some clever algorithms one can shrink each parameter to just 8 or 4 bits (half a byte!), without significant effect on final performance. Read all about it here: https://huggingface.co/blog/4bit-transformers-bitsandbytes.\n",
        "\n",
        "This means that we're going to shrink the size of the base Idefics2-8b model considerably using 4-bit quantization, and then only train a couple of adapter layers on top using LoRa (in float16). This idea of combining LoRa with quantization is called Q-LoRa and is the most memory friendly version.\n",
        "\n",
        "Of course, if you have the memory available, feel free to use full fine-tuning or LoRa without quantization! In case of full fine-tuning, the code snippet below instantiates the model with Flash Attention which considerably speeds up computations.\n",
        "\n",
        "There exist many forms of quantization, here we leverage the [BitsAndBytes integration](https://huggingface.co/docs/transformers/main_classes/quantization#transformers.BitsAndBytesConfig)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "0b428184283c4dcd8f2fa10ff3adbf04"
          ]
        },
        "id": "DQ0nTqbVlvD2",
        "outputId": "044885b1-6d44-46bb-e2c5-16038a34fdb2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0b428184283c4dcd8f2fa10ff3adbf04",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Load model\n",
        "# Three options for training, from the lowest precision training to the highest precision training:\n",
        "# QLoRA: model uses 4-bit quantization, which helps in reducing memory usage while maintaining performance.\n",
        "# Standard LoRA:  model is loaded with standard LoRA adaptations.\n",
        "# Full Fine-Tuning: no memory optimization are done. In that case Flash Attention is used to speed up training, if hardware supports it.\n",
        "\n",
        "if USE_QLORA or USE_LORA:\n",
        "    if USE_QLORA:\n",
        "        bnb_config = BitsAndBytesConfig(\n",
        "            load_in_4bit=True,\n",
        "            bnb_4bit_quant_type=\"nf4\",\n",
        "            bnb_4bit_compute_dtype=torch.float16,\n",
        "        )\n",
        "    model = VideoLlavaForConditionalGeneration.from_pretrained(\n",
        "        MODEL_ID,\n",
        "        torch_dtype=torch.float16,\n",
        "        quantization_config=bnb_config,\n",
        "        device_map=\"auto\",\n",
        "    )\n",
        "else:\n",
        "    # for full fine-tuning, we can speed up the model using Flash Attention\n",
        "    # only available on certain devices, see https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features\n",
        "    model = VideoLlavaForConditionalGeneration.from_pretrained(\n",
        "        MODEL_ID,\n",
        "        torch_dtype=torch.float16,\n",
        "        _attn_implementation=\"flash_attention_2\",\n",
        "        device_map=\"auto\",\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNtOGpvplvD2"
      },
      "source": [
        "## Apply PEFT\n",
        "After loading the base model, we're going to add LoRa adapter layers. We're going to only train these adapter layers (the base model is kept frozen).\n",
        "\n",
        "The difference here with other models are the layers at which we're going to add adapters (in PEFT this is called target_modules). This typically depends a bit on the model.\n",
        "\n",
        "We defined a function to find all linear layers in the model, excluding any layers related to multimodal projections and vision models. This function will help us identify which layers should have LoRA applied. We're going to add adapters to all linear layers of the model (nn.Linear), except for the ones present in the vision encoder and multimodal projector. This means that we're mostly going to adapt the language model part of Video-LLaVa for our use case."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MYDW50LslvD2"
      },
      "outputs": [],
      "source": [
        "def find_all_linear_names(model):\n",
        "    cls = torch.nn.Linear\n",
        "    lora_module_names = set()\n",
        "    multimodal_keywords = ['multi_modal_projector', 'vision_model']\n",
        "    for name, module in model.named_modules():\n",
        "        if any(mm_keyword in name for mm_keyword in multimodal_keywords):\n",
        "            continue\n",
        "        if isinstance(module, cls):\n",
        "            names = name.split('.')\n",
        "            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
        "\n",
        "    if 'lm_head' in lora_module_names: # needed for 16-bit\n",
        "        lora_module_names.remove('lm_head')\n",
        "    return list(lora_module_names)\n",
        "\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=8,\n",
        "    lora_dropout=0.1,\n",
        "    target_modules=find_all_linear_names(model),\n",
        "    init_lora_weights=\"gaussian\",\n",
        ")\n",
        "\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "model = get_peft_model(model, lora_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "QPNYtC1QlvD2",
        "outputId": "014f17a2-ccf2-41d1-c126-cc68cbf034e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeftModel(\n",
              "  (base_model): LoraModel(\n",
              "    (model): VideoLlavaForConditionalGeneration(\n",
              "      (video_tower): CLIPVisionModel(\n",
              "        (vision_model): CLIPVisionTransformer(\n",
              "          (embeddings): CLIPVisionEmbeddings(\n",
              "            (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)\n",
              "            (position_embedding): Embedding(257, 1024)\n",
              "          )\n",
              "          (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder): CLIPEncoder(\n",
              "            (layers): ModuleList(\n",
              "              (0-23): 24 x CLIPEncoderLayer(\n",
              "                (self_attn): CLIPAttention(\n",
              "                  (k_proj): lora.Linear4bit(\n",
              "                    (base_layer): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.1, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1024, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=1024, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (v_proj): lora.Linear4bit(\n",
              "                    (base_layer): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.1, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1024, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=1024, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (q_proj): lora.Linear4bit(\n",
              "                    (base_layer): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.1, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1024, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=1024, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (out_proj): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                )\n",
              "                (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "                (mlp): CLIPMLP(\n",
              "                  (activation_fn): QuickGELUActivation()\n",
              "                  (fc1): Linear4bit(in_features=1024, out_features=4096, bias=True)\n",
              "                  (fc2): Linear4bit(in_features=4096, out_features=1024, bias=True)\n",
              "                )\n",
              "                (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (image_tower): CLIPVisionModel(\n",
              "        (vision_model): CLIPVisionTransformer(\n",
              "          (embeddings): CLIPVisionEmbeddings(\n",
              "            (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)\n",
              "            (position_embedding): Embedding(257, 1024)\n",
              "          )\n",
              "          (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder): CLIPEncoder(\n",
              "            (layers): ModuleList(\n",
              "              (0-23): 24 x CLIPEncoderLayer(\n",
              "                (self_attn): CLIPAttention(\n",
              "                  (k_proj): lora.Linear4bit(\n",
              "                    (base_layer): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.1, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1024, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=1024, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (v_proj): lora.Linear4bit(\n",
              "                    (base_layer): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.1, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1024, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=1024, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (q_proj): lora.Linear4bit(\n",
              "                    (base_layer): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.1, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1024, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=1024, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (out_proj): Linear4bit(in_features=1024, out_features=1024, bias=True)\n",
              "                )\n",
              "                (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "                (mlp): CLIPMLP(\n",
              "                  (activation_fn): QuickGELUActivation()\n",
              "                  (fc1): Linear4bit(in_features=1024, out_features=4096, bias=True)\n",
              "                  (fc2): Linear4bit(in_features=4096, out_features=1024, bias=True)\n",
              "                )\n",
              "                (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (multi_modal_projector): VideoLlavaMultiModalProjector(\n",
              "        (linear_1): Linear4bit(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELUActivation()\n",
              "        (linear_2): Linear4bit(in_features=4096, out_features=4096, bias=True)\n",
              "      )\n",
              "      (language_model): LlamaForCausalLM(\n",
              "        (model): LlamaModel(\n",
              "          (embed_tokens): Embedding(32064, 4096, padding_idx=0)\n",
              "          (layers): ModuleList(\n",
              "            (0-31): 32 x LlamaDecoderLayer(\n",
              "              (self_attn): LlamaSdpaAttention(\n",
              "                (q_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=4096, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (k_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=4096, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (v_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=4096, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (o_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=4096, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (rotary_emb): LlamaRotaryEmbedding()\n",
              "              )\n",
              "              (mlp): LlamaMLP(\n",
              "                (gate_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=11008, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (up_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=4096, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=11008, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (down_proj): lora.Linear4bit(\n",
              "                  (base_layer): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.1, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=11008, out_features=8, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=8, out_features=4096, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (act_fn): SiLU()\n",
              "              )\n",
              "              (input_layernorm): LlamaRMSNorm()\n",
              "              (post_attention_layernorm): LlamaRMSNorm()\n",
              "            )\n",
              "          )\n",
              "          (norm): LlamaRMSNorm()\n",
              "        )\n",
              "        (lm_head): Linear(in_features=4096, out_features=32064, bias=False)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuv04h-DlvD2"
      },
      "source": [
        "## Define PyTorch Lightning Module for Video-LLaVA\n",
        "To streamline the training and evaluation of the Video-LLaVA model, we use [LightningModule](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html), which abstracts away much of the boilerplate code and provides a structured framework for model training. In this section, we define the VideoLlavaModelPLModule, a custom PyTorch Lightning module that encapsulates the model, training loop, validation loop, and optimizer configuration.\n",
        "\n",
        "### VideoLlavaModelPLModule Class\n",
        "\n",
        "The VideoLlavaModelPLModule class inherits from LightningModule and includes methods for training, validation, and optimizer configuration. This setup ensures a clean and efficient training process.\n",
        "\n",
        "Basically, PyTorch Lightning will take care of all device placements (.to(device)) for us, as well as the backward pass, putting the model in training mode, etc.\n",
        "\n",
        "Notice the difference between a training step and an evaluation step:\n",
        "\n",
        "- a training step only consists of a forward pass, in which we compute the cross-entropy loss between the model's next token predictions and the ground truth (in parallel for all tokens, this technique is known as \"teacher forcing\"). The backward pass is handled by PyTorch Lightning.\n",
        "- an evaluation step consists of making the model autoregressively complete the prompt using the generate() method. After that, we compute an evaluation metric between the predicted sequences and the ground truth ones. This allows us to see how the model is improving over the course of training. The metric we use here is accuracy of answering the question.\n",
        "\n",
        "Besides that, we define the optimizer to use (AdamW is a good default choice) and the data loaders, which use the collate functions defined above to batch together items of the PyTorch datasets. Do note that AdamW is a pretty heavy optimizer in terms of memory requirements, but as we're training with QLoRa we only need to store optimizer states for the adapter layers. For full fine-tuning, one could take a look at more memory friendly optimizers such as 8-bit Adam."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WvNdvJ1ylvD2"
      },
      "outputs": [],
      "source": [
        "class VideoLlavaModelPLModule(L.LightningModule):\n",
        "    def __init__(self, config, processor, model):\n",
        "        super().__init__()\n",
        "        self.config = config\n",
        "        self.processor = processor\n",
        "        self.model = model\n",
        "\n",
        "        self.batch_size = config.get(\"batch_size\")\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "\n",
        "        input_ids, attention_mask, pixel_values_videos, labels = batch\n",
        "\n",
        "        outputs = self.model(\n",
        "            input_ids=input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            pixel_values_videos=pixel_values_videos,\n",
        "            labels=labels\n",
        "        )\n",
        "        loss = outputs.loss\n",
        "\n",
        "        self.log(\"train_loss\", loss)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx, dataset_idx=0):\n",
        "\n",
        "        input_ids, attention_mask, pixel_values_videos, answers = batch\n",
        "\n",
        "        # autoregressively generate token IDs\n",
        "        generated_ids = self.model.generate(\n",
        "            input_ids=input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            pixel_values_videos=pixel_values_videos,\n",
        "            max_new_tokens=MAX_LENGTH,\n",
        "            do_sample=False,\n",
        "        )\n",
        "        # turn them back into text, chopping of the prompt\n",
        "        predictions = self.processor.batch_decode(generated_ids[:, input_ids.size(1):], skip_special_tokens=True)\n",
        "\n",
        "        correct = 0\n",
        "        for pred, answer in zip(predictions, answers):\n",
        "            correct += (pred.strip().lower() == answer.lower())\n",
        "        self.log(\"val_accuracy\", correct / len(answers))\n",
        "\n",
        "        return correct\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # you could also add a learning rate scheduler if you want\n",
        "        optimizer = torch.optim.AdamW(self.parameters(), lr=self.config.get(\"lr\"))\n",
        "\n",
        "        return optimizer\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(train_dataset, collate_fn=train_collate_fn, batch_size=self.batch_size, shuffle=True, num_workers=4)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(eval_dataset, collate_fn=eval_collate_fn, batch_size=self.batch_size, shuffle=False, num_workers=4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6iWFbnQlvD2"
      },
      "source": [
        "Let's instantiate it (based on a config dictionary which defines all hyperparameters for training).\n",
        "\n",
        "The batch size was determined based on the compute available.\n",
        "\n",
        "Do note that one can play around with the hyperparameters, I just use good defaults here: 10 epochs, a learning rate of 1e-4 which I found in the original Idefics2 notebook (linked at the top of this notebook), use mixed precision for training (more memory friendly). One could extend this with things like gradient accumulation and gradient checkpointing.\n",
        "\n",
        "I recommend [this guide](https://huggingface.co/docs/transformers/v4.20.1/en/perf_train_gpu_one) which goes over all tips and tricks regarding maximizing fine-tuning performance on consumer hardware."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K2I7vJaDlvD2"
      },
      "outputs": [],
      "source": [
        "config = {\"max_epochs\": 2,\n",
        "          # \"val_check_interval\": 0.2, # how many times we want to validate during an epoch\n",
        "          \"check_val_every_n_epoch\": 1,\n",
        "          \"gradient_clip_val\": 1.0,\n",
        "          \"accumulate_grad_batches\": 8,\n",
        "          \"lr\": 1e-4,\n",
        "          \"batch_size\": 1,\n",
        "          \"num_nodes\": 1,\n",
        "          \"warmup_steps\": 50,\n",
        "}\n",
        "\n",
        "model_module = VideoLlavaModelPLModule(config, processor, model)\n",
        "early_stop_callback = EarlyStopping(monitor=\"val_accuracy\", patience=3, verbose=False, mode=\"min\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-EncDAllvD2"
      },
      "source": [
        "## Define callbacks\n",
        "Optionally, Lightning allows to define so-called [callbacks](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html), which are arbitrary pieces of code that can be executed during training.\n",
        "\n",
        "Here I'm adding a PushToHubCallback which will push the model to the hub at the end of every epoch as well as at the end of training. Do note that you could of course also pass the private=True flag when pushing to the hub, if you wish to keep your model private. Hugging Face also offers the Enterprise Hub so that you can easily share models with your colleagues privately in a secure way.\n",
        "\n",
        "We'll also use the EarlyStopping callback of Lightning, which will automatically stop training once the evaluation metric (edit distance in our case) doesn't improve after 3 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p2KRo-rclvD2"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import HfApi\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "class PushToHubCallback(Callback):\n",
        "    def on_train_epoch_end(self, trainer, pl_module):\n",
        "        print(f\"Pushing model to the hub, epoch {trainer.current_epoch}\")\n",
        "        pl_module.model.push_to_hub(REPO_ID,\n",
        "                                    commit_message=f\"Training in progress, epoch {trainer.current_epoch}\")\n",
        "\n",
        "    def on_train_end(self, trainer, pl_module):\n",
        "        print(f\"Pushing model to the hub after training\")\n",
        "        pl_module.processor.push_to_hub(REPO_ID,\n",
        "                                    commit_message=f\"Training done\")\n",
        "        pl_module.model.push_to_hub(REPO_ID,\n",
        "                                    commit_message=f\"Training done\")\n",
        "\n",
        "early_stop_callback = EarlyStopping(monitor=\"val_edit_distance\", patience=3, verbose=False, mode=\"min\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-ylKSrMlvD2"
      },
      "source": [
        "## Train!\n",
        "Alright, we're set to start training!\n",
        "\n",
        "Do note that this Trainer class supports many more flags! See the [docs](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.trainer.trainer.Trainer.html#lightning.pytorch.trainer.trainer.Trainer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "liZhW7NhlvD3",
        "outputId": "16ab8773-d40f-48f7-e485-b3960c2bea47"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using 16bit Automatic Mixed Precision (AMP)\n",
            "GPU available: True (cuda), used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "trainer = L.Trainer(\n",
        "        default_root_dir=\"/raid/.cache/huggingface/video_llava_demo\",\n",
        "        accelerator=\"gpu\",\n",
        "        devices=[0],\n",
        "        max_epochs=config.get(\"max_epochs\"),\n",
        "        accumulate_grad_batches=config.get(\"accumulate_grad_batches\"),\n",
        "        check_val_every_n_epoch=config.get(\"check_val_every_n_epoch\"),\n",
        "        gradient_clip_val=config.get(\"gradient_clip_val\"),\n",
        "        precision=\"16-mixed\",\n",
        "        limit_val_batches=5,\n",
        "        num_sanity_val_steps=1,\n",
        "        callbacks=[early_stop_callback, PushToHubCallback()],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "referenced_widgets": [
            "",
            "9c99e8a134844fc6bc734868514d92fc"
          ]
        },
        "id": "X1XGM6QflvD3",
        "outputId": "113a2fc3-d7b3-4140-b614-1b46e14b411e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You are using a CUDA device ('NVIDIA A100-SXM4-80GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
            "Missing logger folder: /raid/.cache/huggingface/video_llava_demo/lightning_logs\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [1]\n",
            "\n",
            "  | Name  | Type      | Params\n",
            "------------------------------------\n",
            "0 | model | PeftModel | 3.8 B \n",
            "------------------------------------\n",
            "22.3 M    Trainable params\n",
            "3.8 B     Non-trainable params\n",
            "3.8 B     Total params\n",
            "15,352.594Total estimated model params size (MB)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Sanity Checking: |                                                                                            …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/env0/lib/python3.8/site-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c99e8a134844fc6bc734868514d92fc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training: |                                                                                                   …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validation: |                                                                                                 …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Validation: |                                                                                                 …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "/home/raushan/transformers/src/transformers/feature_extraction_utils.py:142: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
            "  return torch.tensor(value)\n",
            "`Trainer.fit` stopped: `max_epochs=2` reached.\n"
          ]
        }
      ],
      "source": [
        "trainer.fit(model_module)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z6RpsZBClvD6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQPiR5IV89qs"
      },
      "source": [
        "## Inference\n",
        "\n",
        "Let's see if the model has learned something. We'll load the model from the hub first. Notice that, as we only trained adapters on top of the base model, the repository on the hub to which we pushed only contains the weights and configuration of the adapters. This is a very lightweight file smaller than 100 MB.\n",
        "\n",
        "Thanks to the PEFT integration in Transformers, the `from_pretrained` method will automatically load the weights of the base model as well as the adapter weights.\n",
        "\n",
        "To reduce inference costs, we'll again load the model in 4 bits by passing a `quantization_config`, in order to reduce memory usage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jc0JHzIY89qs",
        "outputId": "5845f9fb-0296-47b3-ad21-9dec3fb1093e",
        "colab": {
          "referenced_widgets": [
            "245f0d12878c4efc992829d9b3c78288",
            "13b98dae2e714ac3ac1df6963ebca7a2",
            "b4b5698952c2401dabc5924e1cfa1d6d"
          ]
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "245f0d12878c4efc992829d9b3c78288",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/840 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13b98dae2e714ac3ac1df6963ebca7a2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b4b5698952c2401dabc5924e1cfa1d6d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/89.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoProcessor, BitsAndBytesConfig, VideoLlavaForConditionalGeneration\n",
        "import torch\n",
        "\n",
        "processor = AutoProcessor.from_pretrained(MODEL_ID)\n",
        "\n",
        "# Define quantization config\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "# Load the base model with adapters on top\n",
        "model = VideoLlavaForConditionalGeneration.from_pretrained(\n",
        "    REPO_ID,\n",
        "    torch_dtype=torch.float16,\n",
        "    quantization_config=quantization_config,\n",
        "    device_map=\"auto\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Lqxte3U89qs"
      },
      "source": [
        "Now we're ready to perform inference. We'll take a one example from the validation set here and plot 8 frames to see what is happening in the video."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BNw1jE0n89qw",
        "outputId": "f93b5f96-8565-4c78-b802-74195ae63d9c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "prompt, clip = eval_dataset[1]\n",
        "fig, axarr = plt.subplots(2, 4, figsize = (10, 10))\n",
        "fig.tight_layout()\n",
        "\n",
        "for i in range(2):\n",
        "    for j in range(4):\n",
        "        curr_frame = Image.fromarray(np.uint8(clip[i + j]))\n",
        "        axarr[i, j].imshow(curr_frame)\n",
        "        axarr[i, j].get_xaxis().set_visible(False)\n",
        "        axarr[i, j].get_yaxis().set_visible(False)\n",
        "        axarr[i, j].set_aspect('equal')\n",
        "\n",
        "plt.subplots_adjust(wspace=None, hspace=None)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWWHU1xB89qx"
      },
      "source": [
        "Next we need to prepare the video for the model, along with the text prompt we used during training. We need to apply the chat template to make sure the format is respected.\n",
        "\n",
        "Notice that this is exactly the same as what we did for the evaluation data collate function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BBWCEdiT89qx",
        "outputId": "3d36269e-c9b7-43e2-85ad-1500c62f146d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pixel_values_videos torch.Size([1, 8, 3, 224, 224])\n",
            "input_ids torch.Size([1, 94])\n",
            "attention_mask torch.Size([1, 94])\n"
          ]
        }
      ],
      "source": [
        "answer = prompt[-1]\n",
        "prompt = prompt[:-2]\n",
        "\n",
        "inputs = processor(text=prompt, videos=clip, return_tensors=\"pt\").to(model.device)\n",
        "for k,v in inputs.items():\n",
        "    print(k,v.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2Brzvy689qx"
      },
      "source": [
        "Next we let the model autoregressively generate tokens using the [generate()](https://huggingface.co/docs/transformers/v4.40.1/en/main_classes/text_generation#transformers.GenerationMixin.generate) method, which is recommended for use at inference time. This method feeds each predicted token back into the model as conditioning for each next time step.\n",
        "\n",
        "Do note that there are various ways of decoding text, here we use greedy decoding which is the default. There are various fancier methods such as beam search and top-k sampling. Refer to [this amazing blog post](https://huggingface.co/blog/how-to-generate) for all details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xFSVXwN889qx"
      },
      "outputs": [],
      "source": [
        "# Generate token IDs\n",
        "generated_ids = model.generate(**inputs, max_new_tokens=MAX_LENGTH)\n",
        "\n",
        "# Decode back into text\n",
        "generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bn5yop6589qx",
        "outputId": "537adbf4-56b6-4513-dbb5-58ba0adaa898"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['USER:  \\nAnswer the following multiple choice question based on the video. Question: What will happen if the sphere is removed?\\n A. The blue cylinder collides with the cyan cylinder; B. The blue cylinder collides with the yellow cylinder; C. The green object collides with the yellow object; D. The blue cylinder collides with the green object; \\n ASSISTANT: Answer: A']\n"
          ]
        }
      ],
      "source": [
        "print(generated_texts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ELwUILkk89qx",
        "outputId": "e457c0df-f6d6-4193-e2bc-b91410c6179a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A\n"
          ]
        }
      ],
      "source": [
        "print(answer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bKS_QcmX89qx"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}